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Found 3 papers, 1 papers with code
Measuring AI Systems Beyond Accuracy
Current test and evaluation (T&E) methods for assessing machine learning (ML) system performance often rely on incomplete metrics.
On managing vulnerabilities in AI/ML systems
This paper explores how the current paradigm of vulnerability management might adapt to include machine learning systems through a thought experiment: what if flaws in machine learning (ML) were assigned Common Vulnerabilities and Exposures (CVE) identifiers (CVE-IDs)?
On the human-recognizability phenomenon of adversarially trained deep image classifiers
In this work, we investigate the phenomenon that robust image classifiers have human-recognizable features -- often referred to as interpretability -- as revealed through the input gradients of their score functions and their subsequent adversarial perturbations.
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